With the development of information technology, quantity of information, such as information about news, blogs, and Weibo, is massive and had increasingly grown. The information contains many entities (entity pairs) and entity relation patterns between the entities. If the entities and entity relation patterns therebetween can be extracted from the information, information search, knowledge mining and scientific hypothesis generation or the like can be performed more effectively by using the extracted entities and the extracted entity relation patterns therebetween.
Two methods are commonly used for entity relation mining: one is mining based on a relation defined by a schema, that is, based on a limited given relation and relation-related entity classification, each relation is modeled separately to mine the related entity and relation data, e.g., characters and the parent-child relation between characters. The other is to define a subject in the entities and relation and mine an object in the entities, that is, the subject is mined from related corpus provided by a search engine on the basis of the object and relation searched by a user, for example, the given object and relation are Andy LIU and wife, and then the mining result should be Liqian ZHU.
However, it is difficult for the above two methods to comprehensively and accurately extract the entities and the entity-relation patterns therebetween from massive unstructured information.